mcp-tabular
Enables SQL querying over CSV and Excel files using DuckDB, providing tools to load files, inspect schemas, and run read-only queries via MCP.
README
mcp-tabular
An MCP server that gives any LLM agent SQL over CSV and Excel files. DuckDB-powered, read-only, zero configuration.
Why
Embedding spreadsheets into a vector store gives agents mushy answers; tables chunk badly. The pattern that works in production is SQL-over-file: load the table into an analytical engine and let the agent query it. mcp-tabular packages that pattern as a standard MCP server, so it works with Claude Desktop, Claude Code, or any MCP client.
Agent ──MCP──▶ mcp-tabular ──▶ DuckDB (in-memory, read-only)
tools: load_file · list_tables · describe_table · query · sample_rows
Install & run
pip install -e .
mcp-tabular # stdio transport
Claude Desktop config:
{
"mcpServers": {
"tabular": { "command": "mcp-tabular" }
}
}
Tools
| Tool | Description |
|---|---|
load_file(path, table_name?) |
Load a CSV/XLSX file into an in-memory table. Returns schema + row count. |
list_tables() |
Tables currently loaded. |
describe_table(table) |
Columns, types, null counts, min/max — enough for the agent to write correct SQL. |
query(sql) |
Read-only SELECT. Results capped and returned as markdown. |
sample_rows(table, n) |
Quick peek at representative rows. |
Safety
- Read-only: statements other than
SELECT/WITHare rejected before execution. - Path allow-listing: set
MCP_TABULAR_ROOTto restrict which directory files may be loaded from. - Bounded output: result sets are truncated (default 200 rows) so a bad query can't blow up the agent's context window.
Example session
> load_file("sales_q3.csv")
loaded table 'sales_q3' (8,412 rows). Columns: region VARCHAR, sku VARCHAR, units BIGINT, revenue DOUBLE
> query("SELECT region, SUM(revenue) r FROM sales_q3 GROUP BY 1 ORDER BY r DESC LIMIT 3")
| region | r |
|--------|----------|
| South | 412,050 |
| West | 371,200 |
| East | 298,700 |
Design notes
Built after shipping enterprise agents where tabular Q&A over uploaded files was the highest-volume use case. Schema + sample injection (via describe_table/sample_rows) is what makes agents write correct SQL on the first try.
License
MIT
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